(1) Field of the Invention
The present invention relates to a method for wafer analysis. More particularly, the present invention relates to a method utilizing artificial neural network for wafer analysis.
(2) Prior Art
There are numerous steps in wafer fabrication. In these steps, there must be some measuring and monitoring operations for qualification. The quality of wafer fabrication could be analyzed by some data such as film thickness, electricity, electric resistance, and impurity concentration etc. Then, the problems of product could be found in time to prevent the waste of cost.
In tradition, the way to monitor the quality of wafers is to test the same site continuously for several times and check if any error exists in machine. If any error does exist, the cause of the error is usually examined by human operator with experience to analyze, determine the cause and to improve the process. Although this method could solve problem and get improvement of wafer fabrication, it wastes too much time and manpower. Besides, the risk of manpower does exist. Experience deficiency of analysis and a wrong judgment would cause more waste of time and manpower, and increase the cost.
FIG. 1 shows a schematic diagram of risk analysis for wafer analysis. After all process of wafer fabrication 102 completed, all wafers 101 could be divided into two parts, good wafers 103 and bad wafers 104, respectively. Then, after the step of wafer test 105, the part of good wafers 103 could get the result of pass 106 and the part of bad wafers 104 could get the result of fail 107. However, sometimes wrong judgment could happen. Such as the part of good wafers 103 would get the result of fail 107 and result in risk of product 108; or the part of bad wafers 104 could get the result of pass 106 and result in risk of consumer 109. Both risk of product 108 and risk of consumer 109 resulting from the wrong judgment would cause great loss.
Therefore, it is necessary to provide a method for wafer analysis which could save time and reduce risk of wrong judgment.